Differentiation of Gasoline Samples Using Flame Emission

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Differentiation of Gasoline Samples Using Flame Emission Spectroscopy and Partial Least Squares Discriminate Analysis Jaqueline M. de Paulo,† José E. M. Barros,‡ and Paulo J. S. Barbeira*,† †

Instituto de Ciências Exatas, Departamento de Química, and ‡Escola de Engenharia, Departamento de Engenharia Mecânica, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, 6627, Belo Horizonte, Minas Gerais 31270-901, Brazil ABSTRACT: In this work, flame emission spectroscopy (FES) was combined with the partial least squares discriminant analysis (PLS-DA) method aimed at classifying different types of gasoline retailed in gas stations. In Brazil, three different types of gasoline, namely, regular gasoline (RG), gasoline with additives (AG), and premium gasoline (PG), are available for retail. The legislation and literature does not present methods for the discrimination of these types of gasoline and also lacks an agenda with programs that inspect and/or attest to the presence of additives that distinguish these fuels. For each set of samples, spectra were obtained through FES and, subsequently, the results were treated using PLS-DA. The PLS-DA model was built using only three latent variables (LVs) with accumulated variance of 99.98% in X and 51.05% in Y. The model combining FES to PLS-DA provided excellent sensitivity and specificity values for the calibration set and 100% accuracy in predicting. All samples were analyzed as collected at the gas station, and then the results were obtained in a few seconds without any kind of sample preparation. RON,4 and anti-knock index (AKI) methods.4 In Brazil, the established MON and AKI minimum values, for RG and AG, must be 82.0 and 87.0, respectively. For premium gasoline, only a minimum AKI value of 92.0 is established.3,4 The measure of the resistance of the fuel to auto ignite is heavily influenced by the presence of aromatic hydrocarbons, isoparaffins, olefins, and additives, such as ethanol and esters. Therefore, because of the fact that PG has significant differences in their composition from AG and RG types, the main parameter able to distinguish these classes of gasoline is the octane level. A gasoline with high levels of paraffinic hydrocarbons that have many ramifications, olefins, and aromatics (benzene, toluene, and xylenes) is very resistant to self-detonation, and conversely, the long-chained paraffins with less ramifications and olefins, with more than four carbon atoms, are more susceptible to the phenomena of selfdetonation.5 For the quality control of fuels in addition to standardized methods recommended by the ANP,6 the literature describes alternative methods using different analytical techniques, such as Fourier transform infrared spectrometry (FTIR),7−12 nuclear magnetic resonance (NMR),13,14 Raman spectroscopy,15,16 distillation curves,5,17 and gas chromatography (GC).1,18 These techniques give rise to multivariate responses for each sample.27 Then, a lot of these spectral measurement techniques have also been used to classify gasoline according to type,23,24,27 origin (refinery),20,21,26 brand,22,25 and process.20 Several studies have been conducted linking spectroscopic techniques with chemometric tools, such as partial least squares (PLS) regression, principal component regression (PCR), principal component analysis (PCA), and hierarchical compo-

1. INTRODUCTION The quality control of fuels has become ever so rigorous because of not only the importance of the vehicle performance and the damages to consumers but also the environmental impact caused by its emission. Automotive gasoline is a complex mixture of organic substances acquired through fractional distillation of petroleum, which can be distributed into four classes of hydrocarbons: aromatics, olefins, paraffins and isoparaffins, and naphthenics. Gasoline also contains oxygenated compounds because of the addition of ethanol.1 In Brazil, three types of gasoline are retailed: regular, gasoline with additives, and premium. The regular gasoline (RG) is composed of a mixture of hydrocarbons, coming from the refinery or petrochemical plant, and 20 ± 1% (v/v) anhydrous ethanol, which may vary according to the current legislation, has the simplest composition, and does not receive any kind of additive or dye.2 The gasoline with additives (AG) is the regular gasoline containing detergents/dispersants for the purpose of cleaning the fuel supply system, contributing to the reduction of the formation of deposits within the fuel injection system, the collector, and valve system admission. The premium gasoline (PG) is obtained through the mixture of high-octane naphtha (cracked, alkylated, and reformed naphtha), which provide the product with higher resistance to detonation compared to regular gasoline. To ensure the quality control of fuels retailed in Brazil, the National Agency of Petroleum, Natural Gas, and Biofuels (ANP) regulates and inspects these fuels through the analysis of a few physical chemical properties, such as specific gravity, distillation, motor octane number (MON) and research octane number (RON) values, benzene, aromatics, olefins, and others.32 However, there is no specific assay to distinguish the different types of gasoline. Under Brazilian law, the only parameter that is discriminated is the octane level, which provides an indication of the resistance of the gasoline to auto ignite through the MON,3 © 2014 American Chemical Society

Received: February 11, 2014 Revised: May 19, 2014 Published: May 19, 2014 4355

dx.doi.org/10.1021/ef5003827 | Energy Fuels 2014, 28, 4355−4361

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Table 1. Publications Considering Classification of Gasoline Using Different Techniques technique

application

attenuated total reflection (ATR)−FTIR spectrometry/quadratic discriminant analysis (QDA) Raman spectroscopy and novel R-weighted least squares support vector machine (LSSVM) algorithm physical and chemical properties/PCA, HCA, and LDA distillation curves/multivariate techniques NIR/multivariate techniques NIR/comparison of multivariate techniques GC/HCA and PCA GC/PCA mass spectrometry (MS)/PCA

year

reference

classification of gasoline by quality

2013

24

classification of gasoline brand and origin

2012

25

identification of gasoline origin classification of gasoline by origin classification of gasoline by source (refinery) and type classification of gasoline by source (refinery) and process classification of gasoline by source classification of gasoline by brand classification of gasoline by type (regular and premium)

2007 2008 2008

21 26 19

2010

20

2011 2005 1993

22 27 44

of flame emission spectroscopy (FES) and chemometric tools, such as partial least squares discriminant analysis (PLS-DA), to be able to identify possible irregularities, such as selling one type of fuel as another. During the burning of automotive gasoline, it is possible to observe the formation of a number of radicals, metals, and other compounds, which results in the formation of a characteristic spectrum that can easily be recorded through FES. With the development of molecular spectroscopy, currently, it is possible to characterize the flame through the formation of excited radicals (indicated by *), highlighting the species C2*, CH*, and OH* with concentrated emissions predominantly in the internal cone of the flame when the samples are analyzed through this technique.33 Therefore, the distribution of energy of the species present in the flame can be carried out through band systems frequently described in the literature;34−36 however, usually the spectrum obtained through the burning of gasoline is quite difficult to study once it is not possible to differentiate the bands, because they are usually overlapped. Thus, the employment of an appropriate statistical treatment combined with conventional methods is commonly presented as a proposal for data treatment. Recently, several studies have been described in the literature using FES with different applications. The information obtained from spectral data of intermediary species, which are formed during the burning process, have allowed for the use of FES in studies of kinetic mechanisms applied to processes that involve combustion.34−39 The mapping of radicals, such as CH*, have been used to determine the temperature of the flame as a result of the intense emission band in the region of the visible ultraviolet because of the 2∑− → 2Π and 2Δ → 2Π transitions.35 The Swan band system for the C2* radical is frequently used as a thermometer to estimate the temperature of the gas in the corresponding emission region.34,39−43 Theoretical and experimental aspects about the chemical kinetics may contribute significantly to the quality control of fuels, and they are being clarified through the use of computational techniques10 and the employment of proper optical equipment, capable of registering the continuous spectra of their burn in a flame.16 Recently, Barbeira et al.40 used spectral data acquired using FES, associated with chemometric tools, to predict gasoline fuel adulterations with different solvents. The method provided the identification and quantification of different gasoline adulter-

nent analysis (HCA), allowing for the increasing acquisition of maximum information from the experimental data.13,21,23,28−30 Most of the reported spectroscopic approaches for this kind of analysis are involved with chemometric techniques, as described in Table 1. Among the works cited in the literature, those involving the classification of gasoline by origin (refineries) represent an important factor for both the control of quality parameters and the identification of possible adulterations. Recently, Balabin et al.19,20 pooled the efficiency of different methods of classification-linking results obtained through analysis by near-infrared (NIR) spectroscopy with several chemometric methods, to evaluate adulteration and classification of gasolines according to the refinery, type of refinery process involved in its production (catalytic reforming, isomerization, or hydrocracking), and types of gasoline by sorting with regard to octane as normal (80 octane), regular, and premium. The same authors19,20 extended the studies and found that the best results for classification, using NIR were those obtained by probabilistic analysis using neural networks (PNN), although the results obtained using k-nearest neighbor (k-NN) and support vector machine (SVM) have also proven satisfactory. Following this same line of studies, Barbeira et al. associated results obtained from distillation curves26 and different physicochemical properties21 with chemometric tools, such as PCA, HCA, and LDA. In both studies, the authors have demonstrated the potentiality of these techniques to discriminate gasoline according to the refinery, using the routine results, with other tests being unnecessary. In addition to the works cited for discrimination of gasoline by origin, other works, such as the work recently published by Rudnev et al.,22 using data obtained by GC associated with PCA and cluster analysis are also described. Although several works have been published presenting different applications, few studies refer to discrimination among different types of gasoline.45 In Brazil, AG and PG are typically distinguished from RG by the marker system.31 However, the presence of the marker does not guarantee that the gasoline actually contains additives or that it has a more elaborate differentiated composition as the premium gasoline. As a result of having a more elaborate composition, PG and AG are retailed at a higher price. To favor the commercialization of a high-quality product for the consumer, the aim of this work is to develop a discrimination method of gasoline through the combination 4356


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ants with small calibration and validation errors and also lower values than the values reported by the literature using other spectroscopic techniques. In this work, the spectra obtained for different types of gasoline (RG, AG, and PG) using FES will be combined to the PLS-DA method.

whose octane number values were obtained using engine tests47 and periodically updated with the introduction of new samples. An identical procedure was carried out with the concentration values of different hydrocarbons obtained using chromatography. The accuracy of the values obtained with a commercial spectrometer is usually evaluated by the interlaboratory testing program of ANP, which has more than 20 participants and holds 3 annual rounds.48

2. EXPERIMENTAL SECTION

3. RESULTS AND DISCUSSION Figure 1 shows the spectra recorded for each of the samples of different classes. In this case, it is possible to verify the large

2.1. Samples. A total of 75 samples of gasoline selected as type (RG, AG, and PG) were collected randomly from different gas stations located in the state of Minas Gerais, Brazil. Of this set, 16 samples of RG, 22 samples of AG, and 12 samples of PG were then used to build the calibration model, and for the validation model, 8 samples of RG, 11 samples of AG, and 6 samples of PG were used. For sample separation on calibration and validation sets, the Kennard−Stone algorithm were used. The PLS-DA model was development using PLS2, which is based on the PLS regression in the presence of several dependent Y variables. The PG set had a fewer number of samples because only a small number of gas stations commercialize this product because it has a limited market. 2.2. Experimental Procedures. All of the samples were analyzed using FES with the aid of a nebulizer combustion system of a Varian AA-6 atomic absorption spectrometer. The flame used by this study was obtained through a mixture of vehicular natural gas (VNG) with an approximate composition of 89% CH4 and synthetic industrial air with the equivalent composition of 79% nitrogen and 21% oxygen. The analytic signals were obtained in a few seconds through the continuous emission spectra recorded by the detector from an average of five spectra. The conditions established by this work are presented in Table 2.

Figure 1. Flame emission spectra for different gasoline samples: (gray lines) RG, (dashed lines) AG, and (black lines) PG.

Table 2. Experimental Conditions of Flame Emission Spectrometer parameter

condition

air flow rate [(normal liters per minute (NLPM)] fuel flow rateb (NLPM) sample flow rate (mL min−1) air pressure (bar) fuel pressure (bar)

1.25 11.00 1.0 1.25 5.50

a

a

number of species formed during the burning of these fuels, which form several radicals originated from the fragmentation of molecules in the flame and can be detected in different wavelengths. The burning of aromatic hydrocarbons, olefins, paraffins, naphthenes, and also ethanol, which comprise gasoline, are normally described in the literature through complex mechanisms. A detailed kinetic model for the simulation of gasoline combustion includes about 1550 species and 8000 reactions.33 The general structure of the mechanisms are based on a C1− C4 core and three main blocks: the first block includes all of the main reaction pathways to saturated and unsaturated linear hydrocarbons up to C7; the second block contains the same classes of reactions for branched hydrocarbons from C5 to C8; and the last block includes the reaction or aromatic structures, such as benzene and short-chain alkyl aromatics (toluene, styrene, and others).33 The spectra obtained for the gasoline samples studied (Figure 1) are formed from the detection of different radicals as well as the transitions of each species that overlap each other, which makes the identification of each individual signal quite difficult. Thus, a complex spectrum is observed, and the signals are recorded as a continuous emission typical of a black-body spectrum that does not provide visual discrimination of different classes of gasoline. Therefore, for an enhanced interpretation of these kinds of data, the use of chemometric tools are required. Initially, the spectra obtained for each group of gasoline were arranged into a data matrix, as previously described. Three latent variables (LVs) were used to build the PLS-DA model,

Synthetic air = 79% (v/v) N2 and 21% (v/v) O2. bCH4 = 89% (v/v).

The analytical signals were obtained from the continuous emission spectra recorded by an EPP 2000 (StellarNet, Inc.) spectrometer in the range of 260−860 nm. The data were collected 2 cm from the base of the burner, a position that corresponds to the alignment of the optic fiber with the top of the internal cone of the flame. This position leads to high sensitivity of the measurements, as demonstrated previously.40 For all of the results presented in this work, the signal correspondent to the emission of each sample was subtracted from the flame signal VNG/air. The spectra obtained for each set of samples were organized into a data matrix, so that the signals (variables) observed for each sample were arranged in a m × n matrix, where the samples were arranged in lines and the variables were arranged in columns. Therefore, for each sample (m), the spectra obtained were arranged in a data matrix, so that for each sample, a vector line (m × 1219) was built. The models were constructed with the matrices considering that all data were mean-centered before all analyses and the PLS-DA model was validated using “leave-one-out” cross-validation. A digital automatic densimeter, Anton Paar DMA, model 4500, was used to carry out the specific gravity assays based on ASTM D4052.45 MON and RON values, paraffins, olefins, and aromatics were obtained using a Petrospec GS1000 automatic analyzer based on midinfrared spectroscopy associated with multivariate calibration methods, such as as PLS, PCR, and multiple linear regression (MLR), according to ASTM E1655.46 The equipment database is composed of samples 4357


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through FES, with the majority of these radicals being due to intermediated radicals, generally diatomic molecules, aforementioned and the most intense emissions being due to C2*, CH*, and OH* species.34 From the PLS-DA model, it was possible to produce a loadings graph presented in Figure 3, where it shows the interval correspondent to a larger contribution for the separation of groups in the second LV, which occurs in the range between 500 and 600 nm. The band spectra in Figure 3 are relatively broad, mainly because of the presence of soot and chemiluminescent radicals in the flame, which overlaps the bands of C2* radicals. As a result of the considerable percentage of aromatic compounds found in the gasoline, upon combustion, these radicals can be detected. The presence of these radicals in 550− 600 nm can be further proven upon visual evidence in the yellow flame during the combustion process. Aromatic compounds burned in different proportions in different gasoline, such as RG, AG, and PG, generate compounds, such as phenol (C6H5OH) and acrolein (H2C CH−CHO), that is responsible for producing soot and incandescent radicals.42 Moreover, the burning of these compounds generates acetylene C2H2 as an intermediate product of combustion, which generates very reactive species, such as radical C2H. These species react with oxygenated radicals and produce the radicals CH* and C2*, identified as key species in the classification of gasoline samples according to type. In a simple manner, usually the combustion reactions of hydrocarbons are presented as following the formation of C2* and CH* radicals and are commonly described by43

providing an accumulated variance of 99.98% in X and 51.05% in Y (Table 3). Table 3. Percent Variance Captured by the Regression Model (PLS-DA) X block

Y block

LV

explained variance

accumulated variance

explained variance

accumulated variance

1 2 3

99.43 0.52 0.03

99.43 99.96 99.98

1.56 24.11 25.38

1.56 25.67 51.05

Figure 2 presents the scores graph, where it shows that, although the second LV explains only 0.61% and the third LV

Figure 2. Graph of scores of the first three LVs obtained in the discrimination of gasoline samples: (white triangles) RG, (black circles) AG, and (gray squares) PG.

explains only 0.03% of the total variance of data, these components are important to the observed separation. The premium gasoline presents in its composition a differentiated proportion of saturated compounds (47.6%, v/ v) and olefins (1.8%, v/v) yet a high percentage of aromatic compounds (22.4%, v/v), as shown in Table 4. On the other side, AG and RG gasolines contain a significant percentage of olefins (15%, v/v) and saturated compounds (43%, v/v) and a lower content of aromatics (13%, v/v). The combustion of these samples led to the formation of chemiluminescent radicals and soot, which are easily detected

parameter

RG

AG

PG

0.7495 ± 0.002

0.7493 ± 0.003

0.7475 ± 0.002

13.0 ± 2

13.1 ± 2

22.4 ± 2

15.5 ± 2 43.6 ± 4

15.3 ± 2 43.7 ± 4

1.8 ± 2 47.6 ± 4

81.7 ± 1 95.6 ± 4

81.7 ± 1 95.7 ± 1

88.5 ± 1 101.5 ± 2

(1)

C2H 2 + OH• → C2H* + H 2O

(2)

C2H 2 + OH* → CH* + CO + H 2

(3)

C2H + O → CH* + CO

(4)

C2 + OH → CH* + CO

(5)

C2H 2 + H* → C2* + H 2

(6)

The transitions to C2* radicals, known as the Swan system, are observed in 473.7, 516.2, and 563.5 nm. Through reaction 6, there is the combustion of fuel-rich mixtures that produces results with the increase of the C2* radical and high hydrogen atom concentrations. It also shows that positive weights cover the range between 750 and 850 nm, which characterizes the Phillips band system with the 1Πu → 1∑g+ transition to this same radical and a negative contribution because of the 2∑− → 2 Π transition of the CH* radical.34,40−43 The differentiation of the RG and AG classes is shown in Figure 2, where it is possible to observe that LV3 is responsible for the separation. In this case, it was possible to verify that the presence of additives is the fundamental variable also responsible for this separation. The presence of additives in AG diminishes the superficial tension of the fuel, which facilitates the nebulization process and favors the burning, leading to the formation of radicals in different proportions formed by the burning of the RG. As shown in Figure 3, the separation of the class of samples of RG in LV3 is due to the B3Πg → 3Πu and A3Πg → 3Πu transition, which corresponds to the C2* radical formed during

Table 4. Mean Composition of Gasoline Samples Used in Chemometric Models

specific gravity (g mL−1) aromatics (%, v/v) olefins (%, v/v) saturated compounds (%, v/v) MON RON

C2H 2 + O• → C2H* + O2

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Figure 3. Graphs of loadings obtained for different gasoline types analyzed with FES: (a) LV1, (b) LV2, and (c) LV3.

the burning of saturated hydrocarbons and olefins. In this case, the negative influence as a result of the characteristic signal of the CH* radical between 450 and 500 nm is observed. The PLS-DA model presented in this work indicates that the percentage of classification of gasoline samples was highly significant because the lowest classification was observed for the samples of premium gasoline with 83% for the calibration model (Table 5).

indicates the classification of samples providing 100% accuracy in the prediction of samples from each class RG, AG, and PG. In this way, the results described in this work indicate that the combination of FES and PLS-DA is adequate to classify gasoline samples as well as presenting an extra advantage of being a simple and fast technique, which enables its use as a tool able to contribute to inspection control of the commercialization of these fuels.

Table 5. Sensitivity and Specificity by the PLS-DA Model

4. CONCLUSION

parameter sensitivity specificity sensitivity specificity

(calibration) (calibration) (prediction) (prediction)

class 1 (RG)

class 2 (AG)

class 3 (PG)

0.923 0.882 1.000 1.000

0.864 0.840 1.000 1.000

0.833 0.943 1.000 1.000

This work presented a method based on multivariate calibration (PLS-DA) to discriminate different types of gasoline (regular, with additives, and premium) employing FES. The proposed chemometric methodology provided the discrimination of different types of gasoline without requiring the preparation of samples. The PLS-DA model provided the prediction rate with 100% accuracy for the entire set prediction of samples studied using the spectra obtained from FES. Thus, the results obtained combining FES and PLS-DA indicate high potential for the employment of this methodology with inspection purposes of the commercialization of different types of gasoline.

The sensitivity of the PLS-DA model corresponds to the number of predicted samples belonging to the class divided by the number of samples actually present in the class, and the specificity of the model corresponds to the number of predicted samples considered as not belonging to the class divided by the real number of samples not belonging to the class. Figure 4 4359


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Figure 4. Samples classified by the PLS-DA model: (a) class 1 (RG), (b) class 2 (AG), and (c) class 3 (PG), where (dark gray diamonds) RG calibration samples, (light gray diamonds) AG calibration samples, (white diamonds) PG calibration samples, (black triangles) RG prediction samples, (black circles) AG prediction samples, and (black squares) PG prediction samples.

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(5) Barbeira, P. J. S.; Aleme, H. G.; Mendes, G. Determination of octane numbers in gasoline by distillation curves and partial least squares regression. Fuel 2012, 97, 131−136. (6) ASTM International. ASTM D4814. Standard Specification for Automotive Spark-Ignition Engine Fuel; ASTM International: West Conshohocken, PA, 2010. (7) Andrade, J. M.; Muniategui, S.; Prada, D. Prediction of clean octane numbers of catalytic reformed naphthas using FT-MIR and PLS. Fuel 1997, 76, 1035−1042. (8) Battiste, D. R.; Fry, S. E.; White, F. T.; Scoggins, M. W.; McWillians, T. B. Determination of ethanol in gasohol by infrared spectrometry. Anal. Chem. 1981, 53, 1096−1099. ́ (9) Bessler, E. Determinaçaõ quantitativa de álcool etilico em gasolina por espectrometria no infravermelho. Cienc. Cult. (Campinas, Braz.) 1977, 28, 928−930. (10) Honorato, F. A.; Neto, B. B.; Pimentel, M. F.; Stragevitch, L.; Galvão, R. K. H. Using principal component analysis to find the best calibration settings for simultaneous spectroscopic determination of several gasoline properties. Fuel 2008, 87, 3706−3709. (11) Kelly, J. J.; Barlow, C. H.; Jinguji, T. M.; Callis, J. B. Prediction of gasoline octane numbers from near-infrared spectral features in the range 660−1215 nm. Anal. Chem. 1989, 61, 313−320. (12) Valderrama, P.; Braga, J. W. B.; Poppi, R. J. Variable selection, outlier detection, and figures of merit estimation in a partial leastsquares regression multivariate calibration model. A case study for the determination of quality parameters in the alcohol industry by nearinfrared spectroscopy. J. Agric. Food Chem. 2007, 55, 8331−8338. (13) Monteiro, M. R.; Ambrozin, A. R. P.; Lião, L. M.; Boffo, E. F.; Tavares, L. A.; Ferreira, M. M. C.; Ferreira, A. G. Study of Brazilian

AUTHOR INFORMATION

Corresponding Author

*Telephone: 55-31-3409-5767. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors are thankful to the Funding Authority for Studies and Projects (FINEP), the Foundation for Research Support of the State of Minas Gerais (FAPEMIG), and the National Council for Scientific and Technological Development (CNPq) for the financial support.

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Differentiation of Gasoline Samples Using Flame Emission

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